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Identification of ten variants associated with risk of estrogen-receptor-negative breast cancer

Abstract

Most common breast cancer susceptibility variants have been identified through genome-wide association studies (GWAS) of predominantly estrogen receptor (ER)-positive disease1. We conducted a GWAS using 21,468 ER-negative cases and 100,594 controls combined with 18,908 BRCA1 mutation carriers (9,414 with breast cancer), all of European origin. We identified independent associations at P < 5 × 10−8 with ten variants at nine new loci. At P < 0.05, we replicated associations with 10 of 11 variants previously reported in ER-negative disease or BRCA1 mutation carrier GWAS and observed consistent associations with ER-negative disease for 105 susceptibility variants identified by other studies. These 125 variants explain approximately 16% of the familial risk of this breast cancer subtype. There was high genetic correlation (0.72) between risk of ER-negative breast cancer and breast cancer risk for BRCA1 mutation carriers. These findings may lead to improved risk prediction and inform further fine-mapping and functional work to better understand the biological basis of ER-negative breast cancer.

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Figure 1: Genomic region around the independent ER-negative risk-associated variants 11_108345515_G_A (rs11374964) and 11_108357137_G_A (rs74911261).

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Acknowledgements

We thank all the individuals who took part in these studies and all the researchers, clinicians, technicians and administrative staff who have enabled this work to be carried out.

Genotyping for the OncoArray was funded by the government of Canada through Genome Canada and the Canadian Institutes of Health Research (GPH-129344), the Ministère de l'Économie, de la Science et de l'Innovation du Québec through Génome Québec, the Quebec Breast Cancer Foundation for the PERSPECTIVE project, the US National Institutes of Health (NIH) (1 U19 CA 148065 for the Discovery, Biology and Risk of Inherited Variants in Breast Cancer (DRIVE) project and X01HG007492 to the Center for Inherited Disease Research (CIDR) under contract HHSN268201200008I), Cancer Research UK (C1287/A16563), the Odense University Hospital Research Foundation (Denmark), the National R&D Program for Cancer Control–Ministry of Health and Welfare (Republic of Korea) (1420190), the Italian Association for Cancer Research (AIRC; IG16933), the Breast Cancer Research Foundation, the National Health and Medical Research Council (Australia) and German Cancer Aid (110837).

Genotyping for the iCOGS array was funded by the European Union (HEALTH-F2-2009-223175), Cancer Research UK (C1287/A10710, C1287/A10118 and C12292/A11174]), NIH grants (CA128978, CA116167 and CA176785) and the Post-Cancer GWAS initiative (1U19 CA148537, 1U19 CA148065 and 1U19 CA148112 (GAME-ON initiative)), an NCI Specialized Program of Research Excellence (SPORE) in Breast Cancer (CA116201), the Canadian Institutes of Health Research (CIHR) for the CIHR Team in Familial Risks of Breast Cancer, the Ministère de l'Économie, Innovation et Exportation du Québec (PSR-SIIRI-701), the Komen Foundation for the Cure, the Breast Cancer Research Foundation and the Ovarian Cancer Research Fund.

Combination of the GWAS data was supported in part by the NIH Cancer Post-Cancer GWAS initiative (1 U19 CA 148065) (DRIVE, part of the GAME-ON initiative). LD score regression analysis was supported by grant CA194393.

BCAC is funded by Cancer Research UK (C1287/A16563) and by the European Union via its Seventh Framework Programme (HEALTH-F2-2009-223175, COGS) and the Horizon 2020 Research and Innovation Programme (633784, B-CAST; 634935, BRIDGES). CIMBA is funded by Cancer Research UK (C12292/A20861 and C12292/A11174).

For a full description of funding and acknowledgments, see the Supplementary Note.

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Writing group: R.L.M., K.B.K., K. Michailidou, J. Beesley, S. Kar, S. Lindström, S. Hui, G.D.B., P.D.P.P., F.J.C., D.F.E., P.K., G.C.-T., M.G.-C., M.K.S., A.C.A., J. Simard. Conception and coordination of OncoArray synthesis: D.F.E., A.C.A., J. Simard, C.I.A., J. Byun, S.J.C., E.D., D.J.H., A. Lee, P.D.P.P., J.T., Z.W. OncoArray genotyping: M.A., A.C.A., S.E.B., M.K.B., F.B., G.C.-T., J.M.C., K.F.D., D.F.E., N. Hammell, B. Hicks, K.J., C. Luccarini, L.M., J.M., E.P., J. Romm, M.K.S., X.S., J. Simard, P. Soucy, D.C.T., D.V., J. Vollenweider, L.X., B.Z. OncoArray genotype calling and quality control: X.C., J.D., E.D., D.F.E., K.B.K., J. Lecarpentier, A. Lee, M. Lush. Database management: D. Barrowdale, M.K.B., M.L., L.M., Q.W., R. Keeman, M.K.S. Statistical analysis: K.B.K., K. Michailidou, S. Hui, S. Kar, X.J., A. Rostamianfar, H. Finucane, S. Lindström, D. Barnes, P.K., P.D.P.P., G.D.B., R.L.M., A.C.A., D.F.E. Bioinformatic analysis: J. Beesley, P. Soucy, A. Lemaçon, D. Barnes, F.A.-E., A.D., J. Simard, G.C.-T. Provision of DNA samples and/or phenotypic data: ABCTB Investigators, C.M.A., J. Adlard, S. Agata, S. Ahmed, H.A., J. Allen, K.A., C.B.A., I.L.A., H.A.-C., N.N.A., A.C.A., V.A., N.A., K.J.A., B.A., P.L.A., M.G.E.M.A., J. Azzollini, J. Balmaña, M. Barile, L. Barjhoux, R.B.B., M. Barrdahl, D. Barnes, D. Barrowdale, C. Baynes, M.W.B., J. Beesley, J. Benitez, M. Bermisheva, L. Bernstein, Y.-J.B., K.R.B., M.J.B., C. Blomqvist, W.B., K.B., B. Boeckx, N.V.B., A. Bojesen, S.E.B., M.K.B., B. Bonanni, A. Bozsik, A.R.B., J.S.B., H. Brauch, H. Brenner, B.B.-d.P., C. Brewer, L. Brinton, P.B., A.B.-W., J. Brunet, T.B., B. Burwinkel, S.S.B., A.-L.B.-D., Q.C., T. Caldés, M.A.C., I. Campbell, F.C., O.C., A. Carracedo, B.D.C., J.E.C., L.C., V.C.-M., S.B.C., J.C.-C., S.J.C., X.C., G.C.-T., T.-Y.D.C., J. Chiquette, H.C., K.B.M.C., C.L.C., NBSS Collaborators, T. Conner, D.M.C., J. Cook, E.C.-D., S.C., F.J.C., I. Coupier, D.G.C., A. Cox, S.S.C., K. Cuk, K. Czene, M.B.D., F.D., H.D., R.D., J.D., P.D., O.D., Y.C.D., N.D., S.M.D., C.M.D., S.D., P.-A.D., M. Dumont, A.M.D., L.D., M. Dwek, B.D., T.D., EMBRACE, D.F.E., D.E., R.E., H. Ehrencrona, U.E., B.E., A.B.E., A.H.E., C.E., M.E., L. Fachal, L. Faivre, P.A.F., U.F., J.F., D.F.-J., O.F., H. Flyger, W.D.F., E.F., L. Fritschi, D.F., GEMO Study Collaborators, M. Gabrielson, P. Gaddam, M.D.G., M.G.-D., P.A.G., S.M.G., J. Garber, V.G.-B., M.G.-C., J.A.G.-S., M.M.G., M.G.-V., A. Gehrig, V.G., A.-M.G., G.G.G., G.G., A.K.G., M.S.G., D.E.G., A.G.-N., P. Goodfellow, M.H.G., G.I.G.A., M. Grip, J. Gronwald, A. Grundy, D.G.-K., Q.G., P. Guénel, HEBON, L.H., E. Hahnen, C.A.H., P. Hall, E. Hallberg, U.H., S. Hankinson, T.V.O.H., P. Harrington, S.N.H., J.M.H., C.S.H., A. Hein, S. Helbig, A. Henderson, J.H., P. Hillemanns, S. Hodgson, F.B.H., A. Hollestelle, M.J.H., B. Hoover, J.L.H., C.H., G.H., P.J.H., K.H., D.J.H., N. Håkansson, E.N.I., C.I., M.I., L.I., A.J., P.J., R.J., W.J., U.B.J., E.M.J., N.J., M.J., A.J.-V., R. Kaaks, M. Kabisch, K. Kaczmarek, D.K., K. Kast, R. Keeman, M.J.K., C.M.K., M. Keupers, S. Khan, E.K., J.I.K., J.A.K., I.K., V.-M.K., S.-W.K., P.K., V.N.K., T.A.K., K.B.K., A.K., Y.L., F. Lalloo, K.L., D.L., C. Lasset, C. Lazaro, L.l.M., J. Lecarpentier, M. Lee, A. Lee, E.L., J. Lee, F. Lejbkowicz, F. Lesueur, J. Li, J. Lilyquist, A. Lincoln, A. Lindblom, S. Lindström, J. Lissowska, W.-Y.L., S. Loibl, J. Long, J.T.L., J. Lubinski, C. Luccarini, M. Lush, A.-V.L., R.J.M., T.M., E.M., K.E.M., I.M.K., A. Mannermaa, S. Manoukian, J.E.M., S. Margolin, J.W.M.M., M.E.M., K. Matsuo, D.M., S. Mazoyer, L.M., C. McLean, H.M.-H., A. Meindl, P.M., H.M., K. Michailidou, A. Miller, N.M., R.L.M., G.M., M.M., K. Muir, A.M.M., C. Mulot, S.N., K.L.N., S.L.N., H.N., I.N., D.N., S.F.N., B.G.N., A.N., R.L.N., K. Offit, E.O., O.I.O., J.E.O., H.O., C.O., K. Ong, J.C.O., N.O., A.O., L.O., V.S.P., L.P., S.K.P., T.-W.P.-S., Y.P.-K., R.L., I.S.P., B. Peissel, A.P., J.I.A.P., P.P., J.P., G.P., P.D.P.P., C.M.P., M.P., D.P.-K., B. Poppe, M.E.P., R.P., N.P., D.P., M.A.P., K.P., B.R., P.R., N.R., J. Rantala, C.R.-F., H.S.R., G.R., V.R., K.R., A. Richardson, G.C.R., A. Romero, M.A.R., A. Rudolph, T.R., E.S., J. Sanders, D.P.S., S. Sangrajrang, E.J.S., D.F.S., M.K.S., R.K.S., M.J. Schoemaker, F.S., L. Schwentner, P. Schürmann, C. Scott, R.J.S., S. Seal, L. Senter, C. Seynaeve, M.S., P. Sharma, C.-Y.S., H. Shimelis, M.J. Shrubsole, X.-O.S., L.E.S., J. Simard, C.F.S., C. Sohn, P. Soucy, M.C.S., J.J.S., A.B.S., C. Stegmaier, J. Stone, D.S.-L., G.S., H. Surowy, C. Sutter, A.S., C.I.S., R.M.T., Y.Y.T., J.A.T., M.R.T., M.-I.T., L. Tong, M. Tengström, S.H.T., M.B.T., A.T., M. Thomassen, D.L.T., K. Thöne, M.G.T., L. Tihomirova, M. Tischkowitz, A.E.T., R.A.E.M.T., I.T., D.T., M. Tranchant, T.T., K. Tucker, N.T., H.-U.U., C.V., D.v.d.B., L.V., R.V.-M., A. Vega, A. Viel, J. Vijai, L.W., Q.W., S.W.-G., B.W., C.R.W., J.N.W., C.W., J.W., A.S.W., J.T.W., W.W., R.W., A.W., A.H.W., X.R.Y., D.Y., D.Z., W.Z., A.Z., E.Z., K.K.Z., I.d.-S.-S., kConFab AOCS Investigators, C.J.v.A., E.v.R., A.M.W.v.d.O. All authors read and approved the final version of the manuscript.

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Correspondence to Roger L Milne.

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A list of members and affiliations appears in the Supplementary Note.

A list of members and affiliations appears in the Supplementary Note.

A list of members and affiliations appears in the Supplementary Note.

A list of members and affiliations appears in the Supplementary Note.

A list of members and affiliations appears in the Supplementary Note.

Integrated supplementary information

Supplementary Figure 1 Manhattan plot of associations with breast cancer risk for all imputed and genotyped SNPs using combined data from ER-negative cases and controls and BRCA1 mutation carriers, before excluding known breast cancer susceptibility loci.

Supplementary Figure 2 Manhattan plot of associations with breast cancer risk for all imputed and genotyped SNPs using combined data from ER-negative cases and controls and BRCA1 mutation carriers, after excluding known breast cancer susceptibility loci.

Supplementary Figure 3 Quantile–quantile plot of associations with breast cancer risk for all imputed and genotyped SNPs using combined data from ER-negative cases and controls and BRCA1 mutation carriers.

Supplementary Figure 4 Genomic region around the ER-negative risk-associated variant 2_24739694_CT_T (rs200648189).

One-megabase region showing the statistical significance of all genotyped and imputed SNPs (regional Manhattan plot) and the positions of candidate causal variants in relation to RefSeq annotated genes. Breast cell enhancers overlapping candidate SNPs predicted to target nearby genes by methods including IM-PET and Hnisz are depicted as black bars. Chromatin interactions from ENCODE ChIA-PET experiments in MCF7 cells overlapping candidate variants are depicted as boxes connected by thin lines and are shaded to reflect the confidence score of the interaction. Epigenomic features (derived from publicly available transcription factor ChIP–seq, histone modification ChIP–seq and DNase–seq) that overlap candidate variants are shown as red segments. Density tracks show the summed occurrence of transcription factor ChIP–seq, histone modification ChIP–seq, and DHS peaks at each genomic position. Roadmap Epigenomics Project chromatin state models for HMECs and myoepithelial cells grouped into enhancer, promoter or transcribed annotations are shown as yellow, red and green segments, respectively. Transcript levels in MCF7 cells and HMECs are represented by histograms depicting the mean of combined and normalized RNA–seq expression levels at each genomic position. All MCF7 ChIA-PET (ENCODE) chromatin interactions are represented by black arcs. Published GWAS signals from the NHGRI catalog are shown as green ticks. The last track shows tested OncoArray SNPs (genotyped or imputed) as black ticks and uninterrogated, common (dbSNP138 EUR MAF > 1%) SNPs as red ticks.

Supplementary Figure 5 Genomic region around the ER-negative risk-associated variant 6_130349119_T_C (rs6569648).

One-megabase region showing the statistical significance of all genotyped and imputed SNPs (regional Manhattan plot) and the positions of candidate causal variants in relation to RefSeq annotated genes. Chromatin interactions from ENCODE ChIA-PET experiments in MCF7 cells overlapping candidate variants are depicted as boxes connected by thin lines and are shaded to reflect the confidence score of the interaction. Epigenomic features (derived from publicly available transcription factor ChIP–seq and histone modification ChIP–seq) that overlap candidate variants are shown as red segments. Density tracks show the summed occurrence of transcription factor ChIP–seq, histone modification ChIP–seq and DHS peaks at each genomic position. Roadmap Epigenomics Project chromatin state models for HMECs and myoepithelial cells grouped into enhancer, promoter or transcribed annotations are shown as yellow, red and green segments, respectively. Transcript levels in MCF7 cells and HMECs are represented by histograms depicting the mean of combined and normalized RNA–seq expression levels at each genomic position. All MCF7 ChIA-PET (ENCODE) and HMEC Hi-C52 chromatin interactions are represented by black and blue arcs, respectively. Published GWAS signals from the NHGRI GWAS catalog are shown as green ticks. The last track shows tested OncoArray SNPs (genotyped or imputed) as black ticks and uninterrogated, common (dbSNP138 EUR MAF > 1%) SNPs as red ticks.

Supplementary Figure 6 Genomic region around the ER-negative risk-associated variant 8_170692_T_C (rs66823261).

One-megabase region showing the statistical significance of all genotyped and imputed SNPs (regional Manhattan plot) and the positions of candidate causal variants in relation to RefSeq annotated genes. Chromatin interactions from ENCODE ChIA-PET experiments in MCF7 cells overlapping candidate variants are depicted as boxes connected by thin lines and are shaded to reflect the confidence score of the interaction. Epigenomic features derived from publicly available transcription factor ChIP–seq that overlap candidate variants are shown as red segments. Density tracks show the summed occurrence of transcription factor ChIP–seq, histone modification ChIP–seq and DHS peaks at each genomic position. Roadmap Epigenomics Project chromatin state models for HMECs and myoepithelial cells grouped into enhancer, promoter or transcribed annotations are shown as yellow, red and green segments, respectively. Transcript levels in MCF7 cells and HMECs are represented by histograms depicting the mean of combined and normalized RNA–seq expression levels at each genomic position. All MCF7 ChIA-PET (ENCODE) and HMEC Hi-C52 chromatin interactions are represented by black and blue arcs, respectively. Published GWAS signals from the NHGRI GWAS catalog are shown as green ticks. The last track shows tested OncoArray SNPs (genotyped or imputed) as black ticks and uninterrogated, common (dbSNP138 EUR MAF > 1%) SNPs as red ticks.

Supplementary Figure 7 Genomic region around the ER-negative risk-associated variant 8_124757661_C_T (rs17350191).

One-megabase region showing the statistical significance of all genotyped and imputed SNPs (regional Manhattan plot) and the positions of candidate causal variants in relation to RefSeq annotated genes. Chromatin interactions from ENCODE ChIA-PET experiments in MCF7 cells overlapping candidate variants are depicted as boxes connected by thin lines and are shaded to reflect the confidence score of the interaction. Epigenomic features (derived from publicly available transcription factor ChIP–seq, histone modification ChIP–seq and DNase–seq) that overlap candidate variants are shown as red segments. Density tracks show the summed occurrence of transcription factor ChIP–seq, histone modification ChIP–seq and DHS peaks at each genomic position. Roadmap Epigenomics Project chromatin state models for HMECs and myoepithelial cells grouped into enhancer, promoter or transcribed annotations are shown as yellow, red and green segments, respectively. Transcript levels in MCF7 cells and HMECs are represented by histograms depicting the mean of combined and normalized RNA–seq expression levels at each genomic position. All MCF7 ChIA-PET (ENCODE) and HMEC Hi-C52 chromatin interactions are represented by black and blue arcs, respectively. Published GWAS signals from the NHGRI GWAS catalog are shown as green ticks. The last track shows tested OncoArray SNPs (genotyped or imputed) as black ticks and uninterrogated, common (dbSNP138 EUR MAF > 1%) SNPs as red ticks.

Supplementary Figure 8 Genomic region around the ER-negative risk-associated variant 16_4106788_C_A (rs11076805).

One-megabase region showing the statistical significance of all genotyped and imputed SNPs (regional Manhattan plot) and the positions of candidate causal variants in relation to RefSeq annotated genes. Breast cell enhancers overlapping candidate SNPs predicted to target nearby genes by PreSTIGE53 are depicted as black bars. Chromatin interactions from ENCODE ChIA-PET experiments in MCF7 cells overlapping candidate variants are depicted as boxes connected by thin lines and are shaded to reflect the confidence score of the interaction. Epigenomic features (derived from publicly available transcription factor ChIP–seq and histone modification ChIP–seq) that overlap candidate variants are shown as red segments. Density tracks show the summed occurrence of transcription factor ChIP–seq, histone modification ChIP–seq and DHS peaks at each genomic position. Roadmap Epigenomics Project chromatin state models for HMECs and myoepithelial cells grouped into enhancer, promoter or transcribed annotations are shown as yellow, red and green segments, respectively. Transcript levels in MCF7 cells and HMECs are represented by histograms depicting the mean of combined and normalized RNA–seq expression levels at each genomic position. All MCF7 ChIA-PET (ENCODE) and HMEC Hi-C52 chromatin interactions are represented by black and blue arcs, respectively. Published GWAS signals from the NHGRI GWAS catalog are shown as green ticks. The last track shows tested OncoArray SNPs (genotyped or imputed) as black ticks and uninterrogated, common (dbSNP138 EUR MAF > 1%) SNPs as red ticks.

Supplementary Figure 9 Genomic region around the ER-negative risk-associated variant 18_25401204_A_AT (rs36194942).

One-megabase region showing the statistical significance of all genotyped and imputed SNPs (regional Manhattan plot) and the positions of candidate causal variants in relation to RefSeq annotated genes. Epigenomic features (derived from publicly available transcription factor ChIP–seq, histone modification ChIP–seq and DNase–seq) that overlap candidate variants are shown as red segments. Density tracks show the summed occurrence of transcription factor ChIP–seq, histone modification ChIP–seq and DHS peaks at each genomic position. Roadmap Epigenomics Project chromatin state models for HMECs and myoepithelial cells grouped into enhancer, promoter or transcribed annotations are shown as yellow, red and green segments, respectively. Transcript levels in MCF7 cells and HMECs are represented by histograms depicting the mean of combined and normalized RNA–seq expression levels at each genomic position. All MCF7 ChIA-PET (ENCODE) and HMEC Hi-C52 chromatin interactions are represented by black and blue arcs, respectively. Published GWAS signals from the NHGRI GWAS catalog are shown as green ticks. The last track shows tested OncoArray SNPs (genotyped or imputed) as black ticks and uninterrogated, common (dbSNP138 EUR MAF > 1%) SNPs as red ticks.

Supplementary Figure 10 Genomic region around the ER-negative risk-associated variant 19_11423703_C_G (rs322144).

One-megabase region showing the statistical significance of all genotyped and imputed SNPs (regional Manhattan plot) and the positions of candidate causal variants in relation to RefSeq annotated genes. Chromatin interactions from ENCODE ChIA-PET experiments in MCF7 cells overlapping candidate variants are depicted as boxes connected by thin lines and are shaded to reflect the confidence score of the interaction. Epigenomic features (derived from publicly available transcription factor ChIP–seq and DNase–seq) that overlap candidate variants are shown as red segments. Density tracks show the summed occurrence of transcription factor ChIP–seq, histone modification ChIP–seq and DHS peaks at each genomic position. Roadmap Epigenomics Project chromatin state models for HMECs and myoepithelial cells grouped into enhancer, promoter or transcribed annotations are shown as yellow, red and green segments, respectively. Transcript levels in MCF7 cells and HMECs are represented by histograms depicting the mean of combined and normalized RNA–seq expression levels at each genomic position. All MCF7 ChIA-PET (ENCODE) and HMEC Hi-C52 chromatin interactions are represented by black and blue arcs, respectively. Published GWAS signals from the NHGRI GWAS catalog are shown as green ticks. The last track shows tested OncoArray SNPs (genotyped or imputed) as black ticks and uninterrogated, common (dbSNP138 EUR MAF > 1%) SNPs as red ticks.

Supplementary Figure 11 Genomic region around the ER-negative risk-associated variant 19_30277729_C_T (rs113701136).

One-megabase region showing the statistical significance of all genotyped and imputed SNPs (regional Manhattan plot) and the positions of candidate causal variants in relation to RefSeq annotated genes. Chromatin interactions from ENCODE ChIA-PET experiments in MCF7 cells overlapping candidate variants are depicted as boxes connected by thin lines and are shaded to reflect the confidence score of the interaction. Epigenomic features (derived from publicly available transcription factor ChIP–seq, histone modification ChIP–seq and DNase–seq) that overlap candidate variants are shown as red segments. Density tracks show the summed occurrence of transcription factor ChIP–seq, histone modification ChIP–seq and DHS peaks at each genomic position. Roadmap Epigenomics Project chromatin state models for HMECs and myoepithelial cells grouped into enhancer, promoter or transcribed annotations are shown as yellow, red and green segments, respectively. Transcript levels in MCF7 cells and HMECs are represented by histograms depicting the mean of combined and normalized RNA–seq expression levels at each genomic position. All MCF7 ChIA-PET (ENCODE) and HMEC Hi-C52 chromatin interactions are represented by black and blue arcs, respectively. Published GWAS signals from the NHGRI GWAS catalog are shown as green ticks. The last track shows tested OncoArray SNPs (genotyped or imputed) as black ticks and uninterrogated, common (dbSNP138 EUR MAF > 1%) SNPs as red ticks.

Supplementary Figure 12 Regional eQTL association plot for all variants within 1 Mb of L3MTBL3 and expression of L3MTBL3.

Red dots indicate candidate causal risk variants from the meta-analysis of BCAC ER-negative case–control and CIMBA BRCA1 mutation carrier data.

Supplementary Figure 13 Regional eQTL association plot for all variants within 1 Mb of CDH2 and expression of CDH2.

Red dots indicate candidate causal risk variants from the meta-analysis of BCAC ER-negative case–control and CIMBA BRCA1 mutation carrier data.

Supplementary Figure 14 Enrichment map for pathways enriched in susceptibility to ER-negative breast cancer.

Enriched pathways (enrichment score (ES) ≥ 0.41) are grouped into themes and annotated with genes that appeared to drive the enrichment signal (Online Methods). Shaded circles represent pathways (darker red indicates higher ES, and larger size denotes a greater number of genes in the pathway), and green lines connect those that are most similar in terms of gene set overlap (>70%), with thicker lines denoting greater similarity.

Supplementary Figure 15 Enrichment map of the adenylate cyclase theme, enriched in susceptibility to ER-negative breast cancer.

Shaded circles represent pathways (darker red indicates higher ES, and larger size denotes a greater number of genes in the pathway), and green lines connect those that are most similar in terms of gene set overlap (>70%), with thicker lines denoting greater similarity.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–15 and Supplementary Note

Supplementary Table 1

BCAC studies contributing data on estrogen-receptor-negative cases and controls, by genotyping initiative.

Supplementary Table 2

CIMBA studies contributing data on BRCA1 mutation carriers, by genotyping initiative.

Supplementary Table 3

Ten novel loci associated with risk of estrogen receptor (ER)-negative breast cancer using meta-analysis of BCAC and CIMBA data, by genotyping initiative.

Supplementary Table 4

Results for the two novel ER-negative susceptibility loci at 11q22.3.

Supplementary Table 5

Summary information from bioinformatic and eQTL analyses.

Supplementary Table 6

Data sources for in silico analyses of the ten novel ER-negative breast cancer susceptibility loci.

Supplementary Table 7

Associations for ten novel and ten previously reported (and replicated) loci.

Supplementary Table 8

Associations for ten novel and ten previously reported (and replicated) loci.

Supplementary Table 9

Novel overall breast cancer susceptibility loci from Michailidou et al. (2016): associations with risk of ER-negative breast cancer and breast cancer for BRCA1 mutation carriers.

Supplementary Table 10

Other previously reported (non-ER-negative disease–specific) breast cancer susceptibility loci: associations with risk of ER-negative breast cancer and breast cancer for BRCA1 mutation carriers.

Supplementary Table 11

Associations of 179 breast cancer susceptibility loci with risk for BRCA2 mutation carriers.

Supplementary Table 12

Detailed information about themes and pathways appearing in the enrichment maps (Supplementary Figs. 13 and 14).

Supplementary Table 13

Detailed information about themes and unique genes appearing in the enrichment maps (Supplementary Figs. 13 and 14).

Supplementary Table 14

Pathways enriched in susceptibility to ER-negative breast cancer (enrichment score (ES) ≥ 0.41) but not ER-positive breast cancer (ES < 0).

Supplementary Table 15

Enrichments for ER-negative breast cancer based on summary statistics for combined analysis of BCAC (ER-negative) and CIMBA (BRCA1 mutation carriers) data.

Supplementary Table 16

List of 125 SNPs associated with risk of ER-negative breast cancer.

Supplementary Table 17

List of 39 SNPs associated with breast cancer risk for BRCA1 mutation carriers.

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Milne, R., Kuchenbaecker, K., Michailidou, K. et al. Identification of ten variants associated with risk of estrogen-receptor-negative breast cancer. Nat Genet 49, 1767–1778 (2017). https://doi.org/10.1038/ng.3785

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